# Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


"""Interfaces common to all Neural Modules and Models."""
import copy
import hashlib
import inspect
import os
import shutil
import traceback
from abc import ABC, abstractmethod
from contextlib import contextmanager
from dataclasses import dataclass, field
from enum import Enum
from functools import total_ordering
from pathlib import Path
from typing import Dict, Iterable, List, Optional, Tuple, Union

import hydra
import torch
import wrapt
from huggingface_hub import HfApi
from huggingface_hub import get_token as get_hf_token
from huggingface_hub import hf_hub_download, snapshot_download
from omegaconf import DictConfig, OmegaConf

import nemo
from nemo.core.classes.mixins.hf_io_mixin import HuggingFaceFileIO
from nemo.core.config.templates.model_card import NEMO_DEFAULT_MODEL_CARD_TEMPLATE
from nemo.core.connectors.save_restore_connector import SaveRestoreConnector
from nemo.core.neural_types import NeuralType, NeuralTypeComparisonResult
from nemo.utils import logging
from nemo.utils.cloud import maybe_download_from_cloud
from nemo.utils.data_utils import resolve_cache_dir
from nemo.utils.model_utils import import_class_by_path, maybe_update_config_version

__all__ = ['Typing', 'FileIO', 'Model', 'Serialization', 'typecheck', 'PretrainedModelInfo']

_TYPECHECK_ENABLED = True
_TYPECHECK_SEMANTIC_CHECK_ENABLED = True
# TODO @blisc: Remove _HAS_HYDRA
_HAS_HYDRA = True


def is_typecheck_enabled():
    """
    Getter method for typechecking state.
    """
    return _TYPECHECK_ENABLED


def is_semantic_typecheck_enabled():
    """
    Getter method for typechecking semantics state.
    """
    return _TYPECHECK_SEMANTIC_CHECK_ENABLED


@dataclass
class TypecheckMetadata:
    """
    Metadata class for input/output neural types.

    # Primary attributes
    original_types: Preserve the dictionary of type information provided.

    ignore_collections: For backward compatibility, container support can be disabled explicitly
        using this flag. When set to True, all nesting is ignored and nest-depth checks are skipped.

    # Derived attributed
    mandatory_types: Sub-dictionary of `original_types` which contains only those types which
        are mandatory to include when calling the function.

    base_types: Dictionary of flattened `str: NeuralType` definitions, disregarding the nest level
        details into appropriate arguments.

    container_depth: Dictionary mapping `str: int` - such that the valid depth of the nest of this
        neural type is recorded.

    has_container_types: Bool flag declaring if any of the neural types declares a container nest
        in its signature.

    is_singular_container_type: Bool flag declaring if this is a single Neural Type with a container
        nest in its signature. Required for supporting python list expansion in return statement.

    """

    original_types: Dict[str, NeuralType]
    ignore_collections: bool

    mandatory_types: Dict[str, NeuralType] = field(init=False)
    base_types: Dict[str, NeuralType] = field(init=False)

    container_depth: Dict[str, int] = field(init=False)
    has_container_types: bool = field(init=False)
    is_singular_container_type: bool = field(init=False)

    def __post_init__(self):
        # If even one NeuralType declares a container nest, set to True
        has_container_types = False
        for type_val in self.original_types.values():
            if isinstance(type_val, (list, tuple)):
                has_container_types = True
                break
        self.has_container_types = has_container_types

        # If only one NeuralType is declared, and it declares a container nest, set to True
        if self.has_container_types and len(self.original_types) == 1:
            self.is_singular_container_type = True
        else:
            self.is_singular_container_type = False

        # If container nests are declared, flatten the nest into `base_types`
        # Also compute the nest depth for each of the NeuralTypes
        if self.has_container_types:
            self.base_types = {}
            self.container_depth = {}

            for type_key, type_val in self.original_types.items():
                depth = 0
                while isinstance(type_val, (list, tuple)):
                    if len(type_val) > 1:
                        raise TypeError(
                            f"Neural Type `{type_key}`: {type_val} definition contains more than one element when "
                            "declaring the nested container structure.\n"
                            "Please ensure that you have only 1 NeuralType inside of the entire nested structure "
                            "definition."
                        )

                    type_val = type_val[0]
                    depth += 1

                self.base_types[type_key] = type_val
                self.container_depth[type_key] = depth
        else:
            # Otherwise, simply preserve the original_types and set depth of nest to 0.
            self.base_types = self.original_types
            self.container_depth = {type_key: 0 for type_key in self.base_types.keys()}

        # Compute subset of original_types which are mandatory in the call argspec
        self.mandatory_types = {
            type_key: type_val for type_key, type_val in self.base_types.items() if not type_val.optional
        }


class Typing(ABC):
    """
    An interface which endows module with neural types
    """

    @property
    def input_types(self) -> Optional[Dict[str, NeuralType]]:
        """Define these to enable input neural type checks"""
        return None

    @property
    def output_types(self) -> Optional[Dict[str, NeuralType]]:
        """Define these to enable output neural type checks"""
        return None

    def _validate_input_types(self, input_types=None, ignore_collections=False, **kwargs):
        """
        This function does a few things.

        1) It ensures that len(self.input_types <non-optional>) <= len(kwargs) <= len(self.input_types).
        2) For each (keyword name, keyword value) passed as input to the wrapped function:
            - Check if the keyword name exists in the list of valid self.input_types names.
            - Check if keyword value has the `neural_type` property.
                - If it does, then perform a comparative check and assert that neural types
                    are compatible (SAME or GREATER).
            - Check if keyword value is a container type (list or tuple). If yes,
                then perform the elementwise test of neural type above on each element
                of the nested structure, recursively.

        Args:
            input_types: Either the `input_types` defined at class level, or the local function
                overridden type definition.
            ignore_collections: For backward compatibility, container support can be disabled explicitly
                using this flag. When set to True, all nesting is ignored and nest-depth checks are skipped.
            kwargs: Dictionary of argument_name:argument_value pairs passed to the wrapped
                function upon call.
        """
        if input_types is not None:
            # Precompute metadata
            metadata = TypecheckMetadata(original_types=input_types, ignore_collections=ignore_collections)

            total_input_types = len(input_types)
            mandatory_input_types = len(metadata.mandatory_types)

            # Allow number of input arguments to be <= total input neural types.
            if len(kwargs) < mandatory_input_types or len(kwargs) > total_input_types:
                raise TypeError(
                    f"Number of input arguments provided ({len(kwargs)}) is not as expected. Function has "
                    f"{total_input_types} total inputs with {mandatory_input_types} mandatory inputs."
                )

            for key, value in kwargs.items():
                # Check if keys exists in the defined input types
                if key not in input_types:
                    raise TypeError(
                        f"Input argument {key} has no corresponding input_type match. "
                        f"Existing input_types = {input_types.keys()}"
                    )

                # Perform neural type check
                if (
                    hasattr(value, 'neural_type')
                    and is_semantic_typecheck_enabled()
                    and not metadata.base_types[key].compare(value.neural_type)
                    in (
                        NeuralTypeComparisonResult.SAME,
                        NeuralTypeComparisonResult.GREATER,
                    )
                ):
                    error_msg = [
                        f"{input_types[key].compare(value.neural_type)} :",
                        f"Input type expected : {input_types[key]}",
                        f"Input type found : {value.neural_type}",
                        f"Argument: {key}",
                    ]
                    for i, dict_tuple in enumerate(metadata.base_types[key].elements_type.type_parameters.items()):
                        error_msg.insert(i + 2, f'  input param_{i} : {dict_tuple[0]}: {dict_tuple[1]}')
                    for i, dict_tuple in enumerate(value.neural_type.elements_type.type_parameters.items()):
                        error_msg.append(f'  input param_{i} : {dict_tuple[0]}: {dict_tuple[1]}')
                    raise TypeError("\n".join(error_msg))

                # Perform input ndim check
                if hasattr(value, 'shape'):
                    value_shape = value.shape
                    type_shape = metadata.base_types[key].axes
                    name = key

                    if type_shape is not None and len(value_shape) != len(type_shape):
                        raise TypeError(
                            f"Input shape mismatch occured for {name} in module {self.__class__.__name__} : \n"
                            f"Input shape expected = {metadata.base_types[key].axes} | \n"
                            f"Input shape found : {value_shape}"
                        )

                # Perform recursive neural type check for homogeneous elements
                elif isinstance(value, list) or isinstance(value, tuple):
                    for ind, val in enumerate(value):
                        """
                        This initiates a DFS, tracking the depth count as it goes along the nested structure.
                        Initial depth is 1 as we consider the current loop to be the 1st step inside the nest.
                        """
                        self.__check_neural_type(val, metadata, depth=1, name=key)

    def _attach_and_validate_output_types(self, out_objects, ignore_collections=False, output_types=None):
        """
        This function does a few things.

        1) It ensures that len(out_object) == len(self.output_types).
        2) If the output is a tensor (or list/tuple of list/tuple ... of tensors), it
            attaches a neural_type to it. For objects without the neural_type attribute,
            such as python objects (dictionaries and lists, primitive data types, structs),
            no neural_type is attached.

        Note: tensor.neural_type is only checked during _validate_input_types which is
        called prior to forward().

        Args:
            output_types: Either the `output_types` defined at class level, or the local function
                overridden type definition.
            ignore_collections: For backward compatibility, container support can be disabled explicitly
                using this flag. When set to True, all nesting is ignored and nest-depth checks are skipped.
            out_objects: The outputs of the wrapped function.
        """
        # TODO: Properly implement this
        if output_types is not None:
            # Precompute metadata
            metadata = TypecheckMetadata(original_types=output_types, ignore_collections=ignore_collections)
            out_types_list = list(metadata.base_types.items())
            mandatory_out_types_list = list(metadata.mandatory_types.items())

            # First convert all outputs to list/tuple format to check correct number of outputs
            if isinstance(out_objects, (list, tuple)):
                out_container = out_objects  # can be any rank nested structure
            else:
                out_container = [out_objects]

            # If this neural type has a *single output*, with *support for nested outputs*,
            # then *do not* perform any check on the number of output items against the number
            # of neural types (in this case, 1).
            # This is done as python will *not* wrap a single returned list into a tuple of length 1,
            # instead opting to keep the list intact. Therefore len(out_container) in such a case
            # is the length of all the elements of that list - each of which has the same corresponding
            # neural type (defined as the singular container type).
            if metadata.is_singular_container_type:
                pass

            # In all other cases, python will wrap multiple outputs into an outer tuple.
            # Allow number of output arguments to be <= total output neural types and >= mandatory outputs.

            elif len(out_container) > len(out_types_list) or len(out_container) < len(mandatory_out_types_list):
                raise TypeError(
                    "Number of output arguments provided ({}) is not as expected. "
                    "It should be larger or equal than {} and less or equal than {}.\n"
                    "This can be either because insufficient/extra number of output NeuralTypes were provided,"
                    "or the provided NeuralTypes {} should enable container support "
                    "(add '[]' to the NeuralType definition)".format(
                        len(out_container), len(out_types_list), len(mandatory_out_types_list), output_types
                    )
                )

            # Attach types recursively, if possible
            if not isinstance(out_objects, tuple) and not isinstance(out_objects, list):
                # Here, out_objects is a single object which can potentially be attached with a NeuralType
                try:
                    out_objects.neural_type = out_types_list[0][1]
                except Exception:
                    pass

                # Perform output ndim check
                if hasattr(out_objects, 'shape'):
                    value_shape = out_objects.shape
                    type_shape = out_types_list[0][1].axes
                    name = out_types_list[0][0]

                    if type_shape is not None and len(value_shape) != len(type_shape):
                        raise TypeError(
                            f"Output shape mismatch occured for {name} in module {self.__class__.__name__} : \n"
                            f"Output shape expected = {type_shape} | \n"
                            f"Output shape found : {value_shape}"
                        )

            elif metadata.is_singular_container_type:
                # If only a single neural type is provided, and it defines a container nest,
                # then all elements of the returned list/tuple are assumed to belong to that
                # singular neural type.
                # As such, the "current" depth inside the DFS loop is counted as 1,
                # and subsequent nesting will increase this count.

                # NOTE:
                # As the flag `is_singular_container_type` will activate only for
                # the case where there is 1 output type defined with container nesting,
                # this is a safe assumption to make.
                depth = 1

                # NOTE:
                # A user may chose to explicitly wrap the single output list within an explicit tuple
                # In such a case we reduce the "current" depth to 0 - to acknowledge the fact that
                # the actual nest exists within a wrapper tuple.
                if len(out_objects) == 1 and type(out_objects) == tuple:
                    depth = 0

                for ind, res in enumerate(out_objects):
                    self.__attach_neural_type(res, metadata, depth=depth, name=out_types_list[0][0])
            else:
                # If more then one item is returned in a return statement, python will wrap
                # the output with an outer tuple. Therefore there must be a 1:1 correspondence
                # of the output_neural type (with or without nested structure) to the actual output
                # (whether it is a single object or a nested structure of objects).
                # Therefore in such a case, we "start" the DFS at depth 0 - since the recursion is
                # being applied on 1 neural type : 1 output struct (single or nested output).
                # Since we are guarenteed that the outer tuple will be built by python,
                # assuming initial depth of 0 is appropriate.
                for ind, res in enumerate(out_objects):
                    self.__attach_neural_type(res, metadata, depth=0, name=out_types_list[ind][0])

    def __check_neural_type(self, obj, metadata: TypecheckMetadata, depth: int, name: str = None):
        """
        Recursively tests whether the obj satisfies the semantic neural type assertion.
        Can include shape checks if shape information is provided.

        Args:
            obj: Any python object that can be assigned a value.
            metadata: TypecheckMetadata object.
            depth: Current depth of recursion.
            name: Optional name used of the source obj, used when an error occurs.
        """
        if isinstance(obj, tuple) or isinstance(obj, list):
            for elem in obj:
                self.__check_neural_type(elem, metadata, depth + 1, name=name)
            return  # after processing nest, return to avoid testing nest itself

        type_val = metadata.base_types[name]

        # If nest depth doesnt match neural type structure depth, raise an error
        if not metadata.ignore_collections and depth != metadata.container_depth[name]:
            raise TypeError(
                "While checking input neural types,\n"
                "Nested depth of value did not match container specification:\n"
                f"Current nested depth of NeuralType '{name}' ({type_val}): {depth}\n"
                f"Expected nested depth : {metadata.container_depth[name]}"
            )

        if (
            hasattr(obj, 'neural_type')
            and is_semantic_typecheck_enabled()
            and not type_val.compare(obj.neural_type)
            in (
                NeuralTypeComparisonResult.SAME,
                NeuralTypeComparisonResult.GREATER,
            )
        ):
            raise TypeError(
                f"{type_val.compare(obj.neural_type)} : \n"
                f"Input type expected = {type_val} | \n"
                f"Input type found : {obj.neural_type}"
            )

        # Perform input ndim check
        if hasattr(obj, 'shape'):
            value_shape = obj.shape
            type_shape = type_val.axes

            if type_shape is not None and len(value_shape) != len(type_shape):
                raise TypeError(
                    f"Input shape mismatch occured for {name} in module {self.__class__.__name__} : \n"
                    f"Input shape expected = {type_shape} | \n"
                    f"Input shape found : {value_shape}"
                )

    def __attach_neural_type(self, obj, metadata: TypecheckMetadata, depth: int, name: str = None):
        """
        Recursively attach neural types to a given object - as long as it can be assigned some value.

        Args:
            obj: Any python object that can be assigned a value.
            metadata: TypecheckMetadata object.
            depth: Current depth of recursion.
            name: Optional name used of the source obj, used when an error occurs.
        """
        if isinstance(obj, tuple) or isinstance(obj, list):
            for elem in obj:
                self.__attach_neural_type(elem, metadata, depth=depth + 1, name=name)
            return  # after processing nest, return to avoid argument insertion into nest itself

        type_val = metadata.base_types[name]

        # If nest depth doesnt match neural type structure depth, raise an error
        if not metadata.ignore_collections and depth != metadata.container_depth[name]:
            raise TypeError(
                "While attaching output neural types,\n"
                "Nested depth of value did not match container specification:\n"
                f"Current nested depth of NeuralType '{name}' ({type_val}): {depth}\n"
                f"Expected nested depth : {metadata.container_depth[name]}"
            )

        try:
            obj.neural_type = type_val
        except Exception:
            pass

        # Perform output ndim check
        if hasattr(obj, 'shape'):
            value_shape = obj.shape
            type_shape = type_val.axes

            if type_shape is not None and len(value_shape) != len(type_shape):
                raise TypeError(
                    f"Output shape mismatch occured for {name} in module {self.__class__.__name__} : \n"
                    f"Output shape expected = {type_shape} | \n"
                    f"Output shape found : {value_shape}"
                )


class Serialization(ABC):
    @classmethod
    def from_config_dict(cls, config: 'DictConfig', trainer: Optional['Trainer'] = None):
        """Instantiates object using DictConfig-based configuration"""
        # Resolve the config dict
        if _HAS_HYDRA:
            if isinstance(config, DictConfig):
                config = OmegaConf.to_container(config, resolve=True)
                config = OmegaConf.create(config)
                OmegaConf.set_struct(config, True)

            config = maybe_update_config_version(config)

        # Hydra 0.x API
        if ('cls' in config or 'target' in config) and 'params' in config and _HAS_HYDRA:
            # regular hydra-based instantiation
            instance = hydra.utils.instantiate(config=config)
        # Hydra 1.x API
        elif '_target_' in config and _HAS_HYDRA:
            # regular hydra-based instantiation
            instance = hydra.utils.instantiate(config=config)
        else:
            instance = None
            prev_error = ""
            # Attempt class path resolution from config `target` class (if it exists)
            if 'target' in config:
                target_cls = config["target"]  # No guarantee that this is a omegaconf class
                imported_cls = None
                try:
                    # try to import the target class
                    imported_cls = import_class_by_path(target_cls)
                    # if calling class (cls) is subclass of imported class,
                    # use subclass instead
                    if issubclass(cls, imported_cls):
                        imported_cls = cls
                    accepts_trainer = Serialization._inspect_signature_for_trainer(imported_cls)
                    if accepts_trainer:
                        instance = imported_cls(cfg=config, trainer=trainer)
                    else:
                        instance = imported_cls(cfg=config)
                except Exception as e:
                    # record previous error
                    tb = traceback.format_exc()
                    prev_error = f"Model instantiation failed!\nTarget class:\t{target_cls}" f"\nError(s):\t{e}\n{tb}"
                    logging.debug(prev_error + "\nFalling back to `cls`.")

            # target class resolution was unsuccessful, fall back to current `cls`
            if instance is None:
                try:
                    accepts_trainer = Serialization._inspect_signature_for_trainer(cls)
                    if accepts_trainer:
                        instance = cls(cfg=config, trainer=trainer)
                    else:
                        instance = cls(cfg=config)

                except Exception as e:
                    # report saved errors, if any, and raise
                    if prev_error:
                        logging.error(prev_error)
                    raise e

        if not hasattr(instance, '_cfg'):
            instance._cfg = config
        return instance

    def to_config_dict(self) -> 'DictConfig':
        """Returns object's configuration to config dictionary"""
        if hasattr(self, '_cfg') and self._cfg is not None:
            # Resolve the config dict
            if _HAS_HYDRA and isinstance(self._cfg, DictConfig):
                config = OmegaConf.to_container(self._cfg, resolve=True)
                config = OmegaConf.create(config)
                OmegaConf.set_struct(config, True)

                config = maybe_update_config_version(config)

            self._cfg = config

            return self._cfg
        else:
            raise NotImplementedError(
                'to_config_dict() can currently only return object._cfg but current object does not have it.'
            )

    @classmethod
    def _inspect_signature_for_trainer(cls, check_cls):
        if hasattr(check_cls, '__init__'):
            signature = inspect.signature(check_cls.__init__)
            if 'trainer' in signature.parameters:
                return True
            else:
                return False
        else:
            return False


class FileIO(ABC):
    def save_to(self, save_path: str):
        """
        Standardized method to save a tarfile containing the checkpoint, config, and any additional artifacts.
        Implemented via :meth:`nemo.core.connectors.save_restore_connector.SaveRestoreConnector.save_to`.

        Args:
            save_path: str, path to where the file should be saved.
        """
        raise NotImplementedError()

    @classmethod
    def restore_from(
        cls,
        restore_path: str,
        override_config_path: Optional[str] = None,
        map_location: Optional['torch.device'] = None,
        strict: bool = True,
        return_config: bool = False,
        trainer: Optional['Trainer'] = None,
        save_restore_connector: SaveRestoreConnector = None,
    ):
        """
        Restores model instance (weights and configuration) from a .nemo file

        Args:
            restore_path: path to .nemo file from which model should be instantiated
            override_config_path: path to a yaml config that will override the internal
                config file or an OmegaConf / DictConfig object representing the model config.
            map_location: Optional torch.device() to map the instantiated model to a device.
                By default (None), it will select a GPU if available, falling back to CPU otherwise.
            strict: Passed to load_state_dict. By default True
            return_config: If set to true, will return just the underlying config of the restored
                model as an OmegaConf DictConfig object without instantiating the model.
            trainer: An optional Trainer object, passed to the model constructor.
            save_restore_connector: An optional SaveRestoreConnector object that defines the implementation
                of the restore_from() method.
        """
        raise NotImplementedError()

    @classmethod
    def from_config_file(cls, path2yaml_file: str):
        """
        Instantiates an instance of NeMo Model from YAML config file.
        Weights will be initialized randomly.
        Args:
            path2yaml_file: path to yaml file with model configuration

        Returns:

        """
        if issubclass(cls, Serialization):
            conf = OmegaConf.load(path2yaml_file)
            return cls.from_config_dict(config=conf)
        else:
            raise NotImplementedError()

    def to_config_file(self, path2yaml_file: str):
        """
        Saves current instance's configuration to YAML config file. Weights will not be saved.
        Args:
            path2yaml_file: path2yaml_file: path to yaml file where model model configuration will be saved

        Returns:
        """
        if hasattr(self, '_cfg'):
            self._cfg = maybe_update_config_version(self._cfg)
            with open(path2yaml_file, 'w', encoding='utf-8') as fout:
                OmegaConf.save(config=self._cfg, f=fout, resolve=True)
        else:
            raise NotImplementedError()


@total_ordering
@dataclass
class PretrainedModelInfo:
    pretrained_model_name: str
    description: str
    location: str
    class_: 'Model' = None
    aliases: List[str] = None

    def __repr__(self):
        base = self.__class__.__name__
        extras = (
            "pretrained_model_name={pretrained_model_name},\n\t"
            "description={description},\n\t"
            "location={location}".format(**self.__dict__)
        )

        if self.class_ is not None:
            extras = "{extras},\n\t" "class_={class_}".format(extras=extras, **self.__dict__)

        representation = f"{base}(\n\t{extras}\n)"
        return representation

    def __hash__(self):
        # assumes that locations are unique urls, and therefore their hashes
        # should ideally also be unique
        location_hash = hash(self.location)
        return location_hash

    def __eq__(self, other):
        # another object is equal to self, iff
        # if it's hash is equal to hash(self)
        return hash(self) == hash(other) or self.pretrained_model_name == other.pretrained_model_name

    def __lt__(self, other):
        return self.pretrained_model_name < other.pretrained_model_name


class Model(Typing, Serialization, FileIO, HuggingFaceFileIO):
    """
    Abstract class offering interface which should be implemented by all NeMo models.
    """

    @classmethod
    @abstractmethod
    def list_available_models(cls) -> Optional[List[PretrainedModelInfo]]:
        """
        Should list all pre-trained models available via NVIDIA NGC cloud.
        Note: There is no check that requires model names and aliases to be unique. In the case of a collision, whatever
        model (or alias) is listed first in the this returned list will be instantiated.

        Returns:
            A list of PretrainedModelInfo entries
        """
        pass

    @classmethod
    def get_available_model_names(cls) -> List[str]:
        """
        Returns the list of model names available via NVIDIA NGC cloud,
        to get the complete model description use list_available_models()
        Returns:
            A list of model names
        """
        model_names = []
        if cls.list_available_models() is not None:
            model_names = [model.pretrained_model_name for model in cls.list_available_models()]
        return model_names

    @classmethod
    def from_pretrained(
        cls,
        model_name: str,
        refresh_cache: bool = False,
        override_config_path: Optional[str] = None,
        map_location: Optional['torch.device'] = None,
        strict: bool = True,
        return_config: bool = False,
        trainer: Optional['Trainer'] = None,
        save_restore_connector: SaveRestoreConnector = None,
        return_model_file: Optional[bool] = False,
    ):
        """
        Instantiates an instance of NeMo from NVIDIA NGC cloud
        Use restore_from() to instantiate from a local .nemo file.
        Args:
            model_name: string key which will be used to find the module.
            refresh_cache: If set to True, then when fetching from cloud, this will re-fetch the file
                from cloud even if it is already found in a cache locally.
            override_config_path: path to a yaml config that will override the internal
                config file
            map_location: Optional torch.device() to map the instantiated model to a device.
                By default (None), it will select a GPU if available, falling back to CPU otherwise.
            strict: Passed to torch.load_state_dict. By default true.
            return_config: If set to true, will return just the underlying config of the restored
                model as an OmegaConf DictConfig object without instantiating the model.
            return_model_file: If set to true, will return just the downloaded model file in cache

        Returns:
            A model instance of a particular model class or its underlying config (if return_config is set).
        """
        if save_restore_connector is None:
            save_restore_connector = SaveRestoreConnector()

        # Resolve if the pretrained model name is from NGC or other sources
        # HF Hub source
        if '/' in model_name:
            class_, nemo_model_file_in_cache = cls._get_hf_hub_pretrained_model_info(
                model_name=model_name, refresh_cache=refresh_cache
            )

            # Check if nemo_model_file_in_cache is a directory
            if os.path.isdir(nemo_model_file_in_cache):
                # Update SaveRestoreConnector with the flag to read from an unpacked NeMo folder
                save_restore_connector.model_extracted_dir = nemo_model_file_in_cache

        else:
            # NGC source
            class_, nemo_model_file_in_cache = cls._get_ngc_pretrained_model_info(
                model_name=model_name, refresh_cache=refresh_cache
            )

        if return_model_file:
            return nemo_model_file_in_cache

        instance = class_.restore_from(
            restore_path=nemo_model_file_in_cache,
            override_config_path=override_config_path,
            map_location=map_location,
            strict=strict,
            return_config=return_config,
            trainer=trainer,
            save_restore_connector=save_restore_connector,
        )
        return instance

    @classmethod
    def _get_ngc_pretrained_model_info(cls, model_name: str, refresh_cache: bool = False) -> Tuple[type, str]:
        """
        Resolve the NGC model pretrained information given a model name.
        Assumes the model subclass implements the `list_available_models()` inherited method.

        Args:
            model_name: Str name of the model. Must be the original name or an alias of the model, without any '/'.
            refresh_cache: Bool, determines whether cache must be refreshed (model is re-downloaded).

        Returns:
            A tuple of details describing :
            -   The resolved class of the model. This requires subclass to implement PretrainedModelInfo.class_.
                If the class cannot be resolved, default to the class that called this method.
            -   The path to the NeMo model (.nemo file) in some cached directory.
        """
        location_in_the_cloud = None
        description = None
        class_ = None
        models = cls.list_available_models()
        if models is not None:
            for pretrained_model_info in cls.list_available_models():
                found = False
                if pretrained_model_info.pretrained_model_name == model_name:
                    found = True
                elif pretrained_model_info.aliases is not None:
                    for alias in pretrained_model_info.aliases:
                        if alias == model_name:
                            found = True
                            break
                if found:
                    location_in_the_cloud = pretrained_model_info.location
                    description = pretrained_model_info.description
                    class_ = pretrained_model_info.class_
                    break

        if location_in_the_cloud is None:
            raise FileNotFoundError(
                f"Model {model_name} was not found. Check cls.list_available_models() for the list of all available models."
            )
        filename = location_in_the_cloud.split("/")[-1]
        url = location_in_the_cloud.replace(filename, "")
        cache_dir = Path.joinpath(resolve_cache_dir(), f'{filename[:-5]}')
        # If either description and location in the cloud changes, this will force re-download
        cache_subfolder = hashlib.md5((location_in_the_cloud + description).encode('utf-8')).hexdigest()
        # if file exists on cache_folder/subfolder, it will be re-used, unless refresh_cache is True
        nemo_model_file_in_cache = maybe_download_from_cloud(
            url=url, filename=filename, cache_dir=cache_dir, subfolder=cache_subfolder, refresh_cache=refresh_cache
        )

        logging.info("Instantiating model from pre-trained checkpoint")

        if class_ is None:
            class_ = cls

        return class_, nemo_model_file_in_cache

    @classmethod
    def _get_hf_hub_pretrained_model_info(cls, model_name: str, refresh_cache: bool = False) -> Tuple[type, str]:
        """
        Resolve the HuggingFace Hub model pretrained information given a model name.
        The model name must be of general syntax ``{source_repo}/{model_name}``.

        Note:
            The ``{source_repo}`` need not be ``nvidia``, it can be any public repository, even external to Nvidia.
            This allows public, externally contributed models to be run freely using Nvidia NeMo.

        Args:
            model_name: Str name of the model. Must be the original name or an alias of the model, without any '/'.
            refresh_cache: Bool, determines whether cache must be refreshed (model is re-downloaded).

        Returns:
            A tuple of details describing :
            -   The resolved class of the model. Since the source is external to NeMo, always default to using
                the calling class. Depend on target class resolution by restore_from() for calling the correct class.
            -   The path to the NeMo model (.nemo file) in some cached directory (managed by HF Hub).
        """
        # Resolve the model name without origin for filename
        resolved_model_filename = model_name.split("/")[-1] + '.nemo'

        # Check if api token exists, use if it does
        hf_token = get_hf_token()

        # First check if .nemo file exists in HF
        api = HfApi(token=hf_token)

        # Check if model exists in HF
        nemo_file_exists = api.file_exists(repo_id=model_name, filename=resolved_model_filename, repo_type="model")

        if nemo_file_exists:
            # Try to load the model from the Huggingface Hub
            path = hf_hub_download(
                repo_id=model_name,
                filename=resolved_model_filename,
                library_name='nemo',
                library_version=nemo.__version__,
                force_download=refresh_cache,
                token=hf_token,
            )
        else:
            repo_info = api.repo_info(repo_id=model_name, token=hf_token, files_metadata=True)

            # Download whole HF repo and load entire directory as nemo directory
            cache_dir = Path.joinpath(resolve_cache_dir(), "hf_hub_cache", f'{model_name}')

            # If either description and location in the cloud changes, this will force re-download
            cache_subfolder = []
            # Calculate hash of repo_info
            for sibling in repo_info.siblings:
                filename = sibling.rfilename.lower()
                # Ignore updates to readme when downloading hash
                if "readme" not in filename or "git" not in filename:
                    cache_subfolder.append(sibling.blob_id)
            cache_subfolder = sorted(cache_subfolder)
            cache_subfolder = "".join(cache_subfolder)
            cache_subfolder = hashlib.md5(cache_subfolder.encode('utf-8')).hexdigest()

            # if file exists on cache_folder/subfolder, it will be re-used, unless refresh_cache is True
            save_path = os.path.join(cache_dir, cache_subfolder)

            # If the cache dir already exists, delete it to preserve disk space
            if os.path.exists(cache_dir):
                num_files_in_dir = len(os.listdir(cache_dir))
                if num_files_in_dir > 0:
                    logging.info("Found {} files in cache directory {}".format(num_files_in_dir, cache_dir))
                    logging.info(
                        f"Deleting old cache directory for model `{model_name}` in order to prevent duplicates..."
                    )
                shutil.rmtree(cache_dir, ignore_errors=True)

            if not os.path.exists(save_path):
                logging.info(f"Downloading {model_name} from HuggingFace Hub to path: {save_path}")
                os.makedirs(save_path, exist_ok=True)

            path = snapshot_download(
                repo_id=model_name,
                library_name='nemo',
                library_version=nemo.__version__,
                force_download=refresh_cache,
                cache_dir=save_path,
                local_dir=save_path,
                local_dir_use_symlinks=False,
                token=hf_token,
            )

        # Cannot pre-resolve the specific class without double instantiation (first for config, second for model params)
        # Default to current class, and perform basic class path resolution (handled via restore_from() + target class)
        class_ = cls

        return class_, path

    def generate_model_card(
        self, type: str = "hf", template: str = None, template_kwargs: Optional[Dict[str, str]] = None
    ) -> object:
        """
        Generates a ModelCard for the current model. This method is called when pushing the model to the Hub.

        Returns:
            An object that can be represented as a str representation of the model card, usually in Markdown format.
        """
        if template is None:
            template = copy.deepcopy(NEMO_DEFAULT_MODEL_CARD_TEMPLATE)

        # Populate template kwargs with common model card fields
        if template_kwargs is None:
            template_kwargs = {}

        if type == "hf":
            # Use HuggingFaceFileIO method to generate the huggingface model card
            return self._get_hf_model_card(template=template, template_kwargs=template_kwargs)

        else:
            raise ValueError(f"Model card type {type} not supported.")


class typecheck:
    """
    A decorator which performs input-output neural type checks, and attaches
    neural types to the output of the function that it wraps.

    Requires that the class inherit from :class:`~nemo.core.Typing` in order to perform
    type checking, and will raise an error if that is not the case.

    # Usage (Class level type support)

    .. code-block:: python

        @typecheck()
        def fn(self, arg1, arg2, ...):
            ...

    # Usage (Function level type support)

    .. code-block:: python

        @typecheck(input_types=..., output_types=...)
        def fn(self, arg1, arg2, ...):
            ...

    Points to be noted:

    1) The brackets () in `@typecheck()` are necessary.

        You will encounter a TypeError: __init__() takes 1 positional argument but X
        were given without those brackets.

    2) The function can take any number of positional arguments during definition.

        When you call this function, all arguments must be passed using kwargs only.

    """

    class TypeState(Enum):
        """
        Placeholder to denote the default value of type information provided.
        If the constructor of this decorator is used to override the class level type definition,
        this enum value indicate that types will be overridden.
        """

        UNINITIALIZED = 0

    def __init__(
        self,
        input_types: Union[TypeState, Dict[str, NeuralType]] = TypeState.UNINITIALIZED,
        output_types: Union[TypeState, Dict[str, NeuralType]] = TypeState.UNINITIALIZED,
        ignore_collections: bool = False,
    ):
        self.input_types = input_types
        self.output_types = output_types

        if input_types == self.TypeState.UNINITIALIZED:
            self.input_override = False
        else:
            self.input_override = True

        if output_types == self.TypeState.UNINITIALIZED:
            self.output_override = False
        else:
            self.output_override = True

        self.ignore_collections = ignore_collections

    def __call__(self, wrapped):
        return self.wrapped_call(wrapped)

    def unwrapped_call(self, wrapped):
        return wrapped

    @wrapt.decorator(enabled=is_typecheck_enabled)
    def wrapped_call(self, wrapped, instance: Typing, args, kwargs):
        """
        Wrapper method that can be used on any function of a class that implements :class:`~nemo.core.Typing`.
        By default, it will utilize the `input_types` and `output_types` properties of the class inheriting Typing.

        Local function level overrides can be provided by supplying dictionaries as arguments to the decorator.

        Args:
            input_types: Union[TypeState, Dict[str, NeuralType]]. By default, uses the global `input_types`.
            output_types: Union[TypeState, Dict[str, NeuralType]]. By default, uses the global `output_types`.
            ignore_collections: Bool. Determines if container types should be asserted for depth checks, or
                if depth checks are skipped entirely.

        """
        if instance is None:
            raise RuntimeError("Only classes which inherit nemo.core.Typing can use this decorator !")

        if not isinstance(instance, Typing):
            raise RuntimeError("Only classes which inherit nemo.core.Typing can use this decorator !")

        if hasattr(instance, 'input_ports') or hasattr(instance, 'output_ports'):
            raise RuntimeError(
                "Typing requires override of `input_types()` and `output_types()`, "
                "not `input_ports() and `output_ports()`"
            )

        # Preserve type information
        if self.input_types is typecheck.TypeState.UNINITIALIZED:
            self.input_types = instance.input_types

        if self.output_types is typecheck.TypeState.UNINITIALIZED:
            self.output_types = instance.output_types

        # Resolve global type or local overridden type
        if self.input_override:
            input_types = self.input_types
        else:
            input_types = instance.input_types

        if self.output_override:
            output_types = self.output_types
        else:
            output_types = instance.output_types

        # If types are not defined, skip type checks and just call the wrapped method
        if input_types is None and output_types is None:
            return wrapped(*args, **kwargs)

        # Check that all arguments are kwargs
        if input_types is not None and len(args) > 0:
            raise TypeError("All arguments must be passed by kwargs only for typed methods")

        # Perform rudimentary input checks here
        instance._validate_input_types(input_types=input_types, ignore_collections=self.ignore_collections, **kwargs)

        # Call the method - this can be forward, or any other callable method
        outputs = wrapped(*args, **kwargs)

        instance._attach_and_validate_output_types(
            output_types=output_types, ignore_collections=self.ignore_collections, out_objects=outputs
        )

        return outputs

    @staticmethod
    def set_typecheck_enabled(enabled: bool = True):
        """
        Global method to enable/disable typechecking.

        Args:
            enabled: bool, when True will enable typechecking.
        """
        global _TYPECHECK_ENABLED
        _TYPECHECK_ENABLED = enabled

    @staticmethod
    @contextmanager
    def disable_checks():
        """
        Context manager that temporarily disables type checking within its context.
        """
        typecheck.set_typecheck_enabled(enabled=False)
        try:
            yield
        finally:
            typecheck.set_typecheck_enabled(enabled=True)

    @staticmethod
    def set_semantic_check_enabled(enabled: bool = True):
        """
        Global method to enable/disable semantic typechecking.

        Args:
            enabled: bool, when True will enable semantic typechecking.
        """
        global _TYPECHECK_SEMANTIC_CHECK_ENABLED
        _TYPECHECK_SEMANTIC_CHECK_ENABLED = enabled

    @staticmethod
    @contextmanager
    def disable_semantic_checks():
        """
        Context manager that temporarily disables semantic type checking within its context.
        """
        typecheck.set_semantic_check_enabled(enabled=False)
        try:
            yield
        finally:
            typecheck.set_semantic_check_enabled(enabled=True)

    @staticmethod
    def enable_wrapping(enabled: bool = True):
        typecheck.set_typecheck_enabled(enabled)
        if enabled:
            typecheck.__call__ = nemo.core.classes.common.typecheck.wrapped_call
        else:
            typecheck.__call__ = nemo.core.classes.common.typecheck.unwrapped_call
